By Mr Yang Lin

Journal articles

Lin Y; Ma X; Chu X; Jin Y; Yang Z; Wang Y, 2025, 'LoRA dropout as a sparsity regularizer for overfitting reduction', Knowledge Based Systems, 329, http://dx.doi.org/10.1016/j.knosys.2025.114241

Soundararaj B; Han H; Pettit C; Lin Y, 2025, 'Transforming urban planning through machine learning: A study on planning application classification using natural language processing', Environment and Planning B: Planning and Design, http://dx.doi.org/10.1177/23998083251369142

Lin Y; Han H; Pettit C, 2025, 'Spatial-temporal modeling for urban climate: Enhancing energy prediction with global temporal convolutional attention networks', Urban Climate, http://dx.doi.org/10.1016/j.uclim.2025.102413

Lin Y, 2024, 'Progressive neural network for multi-horizon time series forecasting', Information Sciences, 661, http://dx.doi.org/10.1016/j.ins.2024.120112

Chu X; Ma XY; Lin Y; Wang X; Wang YS; Zhu WW; Mei H, 2024, 'Over-parameterized Graph Neural Network Towards Robust Graph Structure Defending', Ruan Jian Xue Bao Journal of Software, 35, pp. 3878 - 3896, http://dx.doi.org/10.13328/j.cnki.jos.007065

Lin Y; Chu X; Wang Y; Mao W; Zhao J, 2020, 'Cross-Modal Recipe Retrieval with Self-Attention Mechanism', Journal of Frontiers of Computer Science and Technology, 14, pp. 1471 - 1481, http://dx.doi.org/10.3778/j.issn.1673-9418.1912016

Lin Y; Xiao W, 2019, 'Novel Piecewise Linear Formation of Droop Strategy for DC Microgrid', IEEE Transactions on Smart Grid, 10, pp. 6747 - 6755, http://dx.doi.org/10.1109/TSG.2019.2911013

Conference Papers

Ma X; Xu Y; Lin Y; Wang T; Chu X; Gao X; Zhao J; Wang Y, 2025, 'DRESSING UP LLM: EFFICIENT STYLIZED QUESTION-ANSWERING VIA STYLE SUBSPACE EDITING', in 13th International Conference on Learning Representations Iclr 2025, pp. 58818 - 58841

Lin Y; Ma X; Gao X; Li R; Wang Y; Chu X, 2024, 'Combating Label Sparsity in Short Text Topic Modeling via Nearest Neighbor Augmentation', in Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 13762 - 13774, http://dx.doi.org/10.18653/v1/2024.findings-acl.817

Gao X; Lin Y; Li R; Wang Y; Chu X; Ma X; Yu H, 2024, 'Enhancing Topic Interpretability for Neural Topic Modeling Through Topic-Wise Contrastive Learning', in Proceedings International Conference on Data Engineering, pp. 584 - 597, http://dx.doi.org/10.1109/ICDE60146.2024.00051

Chen H; Lin Y; Chen R; Liu Y; Sun Y; Tian Z; Qiu J, 2024, 'Few-Shot Blockchain Domain Named Entity Recognition with Fused Lexical Features', in Mechanisms and Machine Science, pp. 595 - 613, http://dx.doi.org/10.1007/978-3-031-44947-5_48

Ma X; Chu X; Yang Z; Lin Y; Gao X; Zhao J, 2024, 'Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation', in Proceedings of Machine Learning Research, pp. 33686 - 33729

Lin Y, 2023, 'AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series Forecasting', in 2023 IEEE 10th International Conference on Data Science and Advanced Analytics Dsaa 2023 Proceedings, http://dx.doi.org/10.1109/DSAA60987.2023.10302580

Lin Y; Gao X; Chu X; Wang Y; Zhao J; Chen C, 2023, 'Enhancing Neural Topic Model with Multi-Level Supervisions from Seed Words', in Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 13361 - 13377, http://dx.doi.org/10.18653/v1/2023.findings-acl.845

Ma X; Chu X; Wang Y; Lin Y; Zhao J; Ma L; Zhu W, 2023, 'Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications', in Advances in Neural Information Processing Systems

Lin Y; Koprinska I; Rana M, 2021, 'Temporal Convolutional Attention Neural Networks for Time Series Forecasting', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN52387.2021.9534351

Lin Y; Koprinska I; Rana M, 2021, 'SSDNet: State Space Decomposition Neural Network for Time Series Forecasting', in Proceedings IEEE International Conference on Data Mining Icdm, pp. 370 - 378, http://dx.doi.org/10.1109/ICDM51629.2021.00048

Gao J; Lin Y; Wang Y; Wang X; Yang Z; He Y; Chu X, 2020, 'Set-Sequence-Graph: A Multi-View Approach Towards Exploiting Reviews for Recommendation', in International Conference on Information and Knowledge Management Proceedings, pp. 395 - 404, http://dx.doi.org/10.1145/3340531.3411939

Lin Y; Koprinska I; Rana M, 2020, 'Temporal Convolutional Neural Networks for Solar Power Forecasting', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN48605.2020.9206991

Chu X; Lin Y; Wang Y; Wang X; Yu H; Gao X; Tong Q, 2020, 'Distance metric learning with joint representation diversification', in 37th International Conference on Machine Learning Icml 2020, pp. 1940 - 1951

Lin Y; Koprinska I; Rana M; Troncoso A, 2020, 'Solar Power Forecasting Based on Pattern Sequence Similarity and Meta-learning', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 271 - 283, http://dx.doi.org/10.1007/978-3-030-61609-0_22

Lin Y; Koprinska I; Rana M, 2020, 'SpringNet: Transformer and Spring DTW for Time Series Forecasting', in Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, pp. 616 - 628, http://dx.doi.org/10.1007/978-3-030-63836-8_51

Chu X; Lin Y; Wang Y; Wang L; Wang J; Gao J, 2019, 'MLRDA: A multi-task semi-supervised learning framework for drug-drug interaction prediction', in Ijcai International Joint Conference on Artificial Intelligence, pp. 4518 - 4524, http://dx.doi.org/10.24963/ijcai.2019/628

Lin Y; Koprinska I; Rana M; Troncoso A, 2019, 'Pattern Sequence Neural Network for Solar Power Forecasting', in Communications in Computer and Information Science, pp. 727 - 737, http://dx.doi.org/10.1007/978-3-030-36802-9_77

Lin Y; Wickramasinghe HR; Konstantinou G; Wickramasinghe H, 2018, 'Hardware-in-the-loop implementation of a hybrid circuit breaker controller for MMC-based HVDC systems', in Asia Pacific Power and Energy Engineering Conference Appeec, Institute of Electrical and Electronics Engineers (IEEE), MALAYSIA, Kota Kinabalu, pp. 19 - 24, presented at 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), MALAYSIA, Kota Kinabalu, 07 October 2018 - 10 October 2018, http://dx.doi.org/10.1109/APPEEC.2018.8566399

Preprints

Lin Y, 2023, AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series Forecasting, http://dx.doi.org/10.1109/DSAA60987.2023.10302580

Lin Y, 2023, Progressive Neural Network for Multi-Horizon Time Series Forecasting, http://dx.doi.org/10.1016/j.ins.2024.120112

Lin Y; Koprinska I; Rana M, 2021, SSDNet: State Space Decomposition Neural Network for Time Series Forecasting, http://arxiv.org/abs/2112.10251v1

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